Advanced 3D Localization by Fusing Measurements from GPS, Inertial and Vision Sensors

In this paper we present an efficient algorithm for estimating the 3D localization in an urban environments by fusing measurements from CPS receiver, inertial sensor and vision. Such hybrid sensor is important for numerous applications including outdoor mobile augmented reality and 3D robot localization. Our approach is based on non-linear filtering of these complementary sensors using a multi-rate Extended Kalman Filter. Our main contributions concern the modeling of the sensor fusion and the development of an efficient approach for camera pose tracking using only natural features. This method improves the accuracy of the estimated 3D localization. We evaluated the performances of our approach and demonstrated its effectiveness through experiments on real data.